22,617 results match your criteria learning curve

Predicting Malignancy with Pediatric Thyroid Nodules: Early Experience in Machine Learning for Clinical Decision Support.

J Clin Endocrinol Metab 2021 Jun 23. Epub 2021 Jun 23.

Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada.

Background: Papillary thyroid carcinoma is the most common endocrine malignancy. Since most nodules are benign, the challenge for the clinician is to identify those most likely to harbour malignancy while limiting exposure to surgical risks among those with benign nodules.

Methods: Random Forests (augmented to select features based on our clinical measure of interest), in conjunction with interpretable rule sets, were used on demographic, ultrasound and biopsy data of thyroid nodules from children <18 years at a tertiary pediatric hospital. Read More

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Harefuah 2021 Jun;160(6):361-366

The Orthopedic Division, Rambam Medical Center, Haifa, Israel.

Introduction: Over the years total hip arthroplasty had turned from a procedure indicated for elderly, low functioning patients, into a procedure indicated also for younger patients who are interested in a well-functioning hip joint to maintain their active lifestyle. Previously, posterior approach was most commonly utilized, however in recent years, due to the accumulating evidence regarding the advantages of the anterior approach, an increase in the prevalence of the anterior approach is noted. In contrast to other surgical approaches, the anterior approach "respects" inter-muscular and inter-nervous planes and therefore is perceived as associates with less pain, faster rehabilitation, and a good stability relative to other approaches. Read More

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Harefuah 2021 Jun;160(6):358-360

Orthopaedic Surgery Division, Hillel Yaffe Medical Center, Israel.

Introduction: Both the direct and anterior approach (DAA) for total hip replacement (THR) surgery have gained much popularity in recent years. The suggested benefits of the muscle-sparing and nerve-sparing approach which could lead to a faster and easier recovery process, have led to an increase in the prevalence of the anterior approach in THR. These potential benefits have drawn the attention of both orthopaedic surgeons as well as patients as a faster return to their active lifestyle. Read More

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Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration.

Acta Ophthalmol 2021 Jun 23. Epub 2021 Jun 23.

Department of Ophthalmology, College of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.

Purpose: This study aimed to determine the efficacy of a multimodal deep learning (DL) model using optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images for the assessment of choroidal neovascularization (CNV) in neovascular age-related macular degeneration (AMD).

Methods: This retrospective and cross-sectional study was performed at a multicentre, and the inclusion criteria were age >50 years and a diagnosis of typical neovascular AMD. The OCT and OCTA data for an internal data set and two external data sets were collected. Read More

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A preliminary study using spinal MRI-based radiomics to predict high-risk cytogenetic abnormalities in multiple myeloma.

Radiol Med 2021 Jun 22. Epub 2021 Jun 22.

Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.

Objectives: We aimed to investigate the feasibility of predicting high-risk cytogenetic abnormalities (HRCAs) in patients with multiple myeloma (MM) using a spinal MRI-based radiomics method.

Materials And Methods: In this retrospective study, we analyzed the radiomic features of 248 lesions (HRCA [n = 111] and non-HRCA [n = 137]) using T1WI, T2WI, and fat suppression T2WI. To construct the radiomics model, the top nine most frequent radiomic features were selected using logistic regression (LR) machine-learning processes. Read More

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Radiation exposure in fluoroscopy-guided anterior total hip arthroplasty: a systematic review.

Eur J Orthop Surg Traumatol 2021 Jun 22. Epub 2021 Jun 22.

Department of Orthopaedic Surgery and Rehabilitation Medicine, Downstate Medical Center, State University of New York (SUNY), 450 Clarkson Ave, MSC 30, Brooklyn, NY, 11203, USA.

Purpose: To investigate the average fluoroscopy time, as well as the patient and surgical staff average radiation exposure in the context of intraoperative fluoroscopy use during anterior total hip arthroplasty (THA).

Methods: PubMed, Cochrane, Embase, Web of Science and Scopus were systematically searched for studies pertaining to intraoperative anterior THA fluoroscopy (PROSPERO ID 258049). The comprehensive literary search was conducted using "THA," "fluoroscopy" and "radiation exposure" as the search criteria, which resulted in 187 total papers. Read More

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Analyzing the learning curves of a novice and an experienced urologist for transrectal magnetic resonance imaging-ultrasound fusion prostate biopsy.

Transl Androl Urol 2021 May;10(5):1956-1965

Urology Department, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.

Background: The aim of the current study was to evaluate and compare the learning curves of transrectal magnetic resonance imaging-ultrasound fusion biopsy for two urologists with different backgrounds (Operator 1: experienced, self-trained and Operator 2: novice, trained by a mentor/MRI reading courses).

Methods: A cohort of 400 patients who underwent fusion prostate biopsy in our department was analyzed. The learning curves were assessed in terms of overall and clinically significant prostate cancer (PCa) detection rates, percentage of positive biopsy cores/targeted and the percentage of PCa tissue on positive targeted cores. Read More

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Detection of the location of pneumothorax in chest X-rays using small artificial neural networks and a simple training process.

Sci Rep 2021 Jun 22;11(1):13054. Epub 2021 Jun 22.

Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea.

The purpose of this study was to evaluate the diagnostic performance achieved by using fully-connected small artificial neural networks (ANNs) and a simple training process, the Kim-Monte Carlo algorithm, to detect the location of pneumothorax in chest X-rays. A total of 1,000 chest X-ray images with pneumothorax were taken randomly from NIH (the National Institutes of Health) public image database and used as the training and test sets. Each X-ray image with pneumothorax was divided into 49 boxes for pneumothorax localization. Read More

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Identifying genetic risk variants associated with noise-induced hearing loss based on a novel strategy for evaluating individual susceptibility.

Hear Res 2021 Jun 6;407:108281. Epub 2021 Jun 6.

Department of Otolaryngology-Head and Neck Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai 200233, China; Otolaryngology Institute of Shanghai Jiao Tong University, Shanghai 200233, China; Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, China.

Background: The overall genetic profile for noise-induced hearing loss (NIHL) remains elusive. Herein we proposed a novel machine learning (ML) based strategy to evaluate individual susceptibility to NIHL and identify the underlying genetic risk variants based on a subsample of participants with extreme phenotypes.

Methods: Five features (age, sex, cumulative noise exposure [CNE], smoking, and alcohol drinking status) of 5,539 shipbuilding workers from large cross-sectional surveys were included in four ML classification models to predict their hearing levels. Read More

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Deep learning radiomics-based prediction of distant metastasis in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy: A multicentre study.

EBioMedicine 2021 Jun 19;69:103442. Epub 2021 Jun 19.

Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China. Electronic address:

Background: Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics.

Methods: We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals. Read More

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Predicting reintervention after thoracic endovascular aortic repair of Stanford type B aortic dissection using machine learning.

Eur Radiol 2021 Jun 22. Epub 2021 Jun 22.

Department of Catheterization Lab, Guangdong Cardiovascular Institute, Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, Guangzhou, 510080, People's Republic of China.

Objectives: To construct models for predicting reintervention after thoracic endovascular aortic repair (TEVAR) of Stanford type B aortic dissection (TBAD).

Methods: A total of 192 TBAD patients who underwent TEVAR were included; 68 (35.4%) had indications for reintervention. Read More

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Comparison of Radiomic Models Based on Different Machine Learning Methods for Predicting Intracerebral Hemorrhage Expansion.

Clin Neuroradiol 2021 Jun 22. Epub 2021 Jun 22.

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.

Purpose: The objective of this study was to predict hematoma expansion (HE) by radiomic models based on different machine learning methods and determine the best radiomic model through the comparison.

Method: A total of 108 patients with intracerebral hemorrhage were retrospectively evaluated. Images of baseline non-contrast computed tomography (NCCT) and follow-up NCCT scan within 24 h were retrospectively reviewed. Read More

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Resident Involvement in Posterior Lumbar Interbody Fusion is Associated With Increased Readmissions and Operative Time, But No Increased Short-term Risks.

Clin Spine Surg 2021 Jul;34(6):E364-E369

Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.

Study Design: A retrospective cohort study.

Objective: The aim was to compare rates of adverse events and additional posterior lumbar interbody fusion (PLIF) cases assisted by residents versus cases performed solely by an orthopedic attending.

Summary Of Background Data: PLIF is a widely accepted surgical technique for the management of a variety of spinal conditions requiring spinal stabilization and fusion. Read More

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Convolutional neural networks for the classification of chest X-rays in the IoT era.

Multimed Tools Appl 2021 Jun 17:1-15. Epub 2021 Jun 17.

Artificial Intelligence Department, Near East University, Nicosia, North Cyprus via Mersin 10, Turkey.

Chest X-ray medical imaging technology allows the diagnosis of many lung diseases. It is known that this technology is frequently used in hospitals, and it is the most accurate way of detecting most thorax diseases. Radiologists examine these images to identify lung diseases; however, this process can require some time. Read More

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Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets.

J Cardiol 2021 Jun 18. Epub 2021 Jun 18.

The Mina and Everard Goodman Faculty of Life Sciences. Adult Bone Marrow Transplantation Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Background: Various prognostic models for mortality prediction following ST-segment elevation myocardial infarction (STEMI) have been developed over the past two decades. Our group has previously demonstrated that machine learning (ML)-based models can outperform known risk scores for 30-day mortality post-STEMI. The study aimed to redevelop an ML-based random forest prediction model for 30-day mortality post-STEMI and externally validate it on a large cohort. Read More

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Artificial Intelligence Image Recognition of Melanoma and Basal Cell Carcinoma in Racially Diverse Populations.

J Dermatolog Treat 2021 Jun 22:1-17. Epub 2021 Jun 22.

Dermatology & Plastic Surgery Institute, Cleveland Clinic, Cleveland, OH 44195, USA.

Background: Artificial intelligence (AI) image recognition models have been relatively successful in diagnosing cutaneous manifestations in individuals with light skin tone. However, when these models are tested on the same cutaneous manifestations in individuals with darker or brown skin tone, the performance of the model drops due to a paucity of such images available for model training.

Objective: The objective of this study was to improve the performance of AI models in recognizing cutaneous diseases in individuals with darker skin tone. Read More

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A novel algorithm for the computation of the diastolic pressure ratio in the invasive assessment of the functional significance of coronary artery disease.

Panminerva Med 2021 Jun;63(2):206-213

Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University, Latina, Italy -

Background: Invasive functional assessment is a mainstay in the management of patients with coronary artery disease (CAD), but there is uncertainty on the comparative accuracy of diagnostic indices of functional significance. We aimed to validate the diagnostic performance of a novel non-hyperemic diastolic pressure ratio (dPR).

Methods: We performed a retrospective analysis including two separate registries (VERIFY 2, Latina, Italy) of patients in whom functional indices were measured for lesions with angiographically moderate severity. Read More

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Deep learning for diagnosing osteonecrosis of the femoral head based on magnetic resonance imaging.

Comput Methods Programs Biomed 2021 Jun 5;208:106229. Epub 2021 Jun 5.

Longwood Valley Medical Technology Co. Ltd, China.

Background And Objective: Early-stage osteonecrosis of the femoral head (ONFH) can be difficult to detect because of a lack of symptoms. Magnetic resonance imaging (MRI) is sufficiently sensitive to detect ONFH; however, the diagnosis of ONFH requires experience and is time consuming. We developed a fully automatic deep learning model for detecting early-stage ONFH lesions on MRI. Read More

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Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh.

J Environ Manage 2021 Jun 18;295:113086. Epub 2021 Jun 18.

School of Earth and Planetary Sciences, Curtin University, Kent St, Bentley, WA, 6102, Australia. Electronic address:

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e. Read More

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Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.

PLoS One 2021 21;16(6):e0253239. Epub 2021 Jun 21.

Department of International Health, International Vaccine Access Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.

Background: The World Health Organization (WHO)-defined radiological pneumonia is a preferred endpoint in pneumococcal vaccine efficacy and effectiveness studies in children. Automating the WHO methodology may support more widespread application of this endpoint.

Methods: We trained a deep learning model to classify pneumonia CXRs in children using the World Health Organization (WHO)'s standardized methodology. Read More

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External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.

JAMA Intern Med 2021 Jun 21. Epub 2021 Jun 21.

Department of Internal Medicine, University of Michigan Medical School, Ann Arbor.

Importance: The Epic Sepsis Model (ESM), a proprietary sepsis prediction model, is implemented at hundreds of US hospitals. The ESM's ability to identify patients with sepsis has not been adequately evaluated despite widespread use.

Objective: To externally validate the ESM in the prediction of sepsis and evaluate its potential clinical value compared with usual care. Read More

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Use of Multiprognostic Index Domain Scores, Clinical Data, and Machine Learning to Improve 12-Month Mortality Risk Prediction in Older Hospitalized Patients: Prospective Cohort Study.

J Med Internet Res 2021 Jun 21;23(6):e26139. Epub 2021 Jun 21.

College of Medicine and Public Health, Flinders University, Adelaide, Australia.

Background: The Multidimensional Prognostic Index (MPI) is an aggregate, comprehensive, geriatric assessment scoring system derived from eight domains that predict adverse outcomes, including 12-month mortality. However, the prediction accuracy of using the three MPI categories (mild, moderate, and severe risk) was relatively poor in a study of older hospitalized Australian patients. Prediction modeling using the component domains of the MPI together with additional clinical features and machine learning (ML) algorithms might improve prediction accuracy. Read More

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Multitask prediction of organ dysfunction in the intensive care unit using sequential subnetwork routing.

J Am Med Inform Assoc 2021 Jun 21. Epub 2021 Jun 21.

Google Health, London, United Kingdom.

Objective: Multitask learning (MTL) using electronic health records allows concurrent prediction of multiple endpoints. MTL has shown promise in improving model performance and training efficiency; however, it often suffers from negative transfer - impaired learning if tasks are not appropriately selected. We introduce a sequential subnetwork routing (SeqSNR) architecture that uses soft parameter sharing to find related tasks and encourage cross-learning between them. Read More

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Risk predicting for acute coronary syndrome based on machine learning model with kinetic plaque features from serial coronary computed tomography angiography.

Eur Heart J Cardiovasc Imaging 2021 Jun 21. Epub 2021 Jun 21.

Department of Geriatric Cardiology & National Clinical Research Center for Geriatric Diseases, Second Medical Center of Chinese PLA General Hospital, 28# Fuxing road, Haidian district, Beijing 100853, China.

Aims: More patients with suspected coronary artery disease underwent coronary computed tomography angiography (CCTA) as gatekeeper. However, the prospective relation of plaque features to acute coronary syndrome (ACS) events has not been previously explored.

Methods And Results: One hundred and one out of 452 patients with documented ACS event and received more than once CCTA during the past 12 years were recruited. Read More

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Cochlear implant surgery: Learning curve in virtual reality simulation training and transfer of skills to a 3D-printed temporal bone - A prospective trial.

Cochlear Implants Int 2021 Jun 19:1-8. Epub 2021 Jun 19.

Copenhagen Hearing and Balance Centre, Department of Otorhinolaryngology-Head & Neck Surgery and Audiology, Rigshospitalet, Copenhagen, Denmark.

Objective: Mastering Cochlear Implant (CI) surgery requires repeated practice, preferably initiated in a safe - i.e. simulated - environment. Read More

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Can plants fool artificial intelligence? Using machine learning to compare between bee orchids and bees.

Plant Signal Behav 2021 Jun 20:1935605. Epub 2021 Jun 20.

School of Biological Sciences, Universiti Sains Malaysia, Minden, Penang, Malaysia.

Bee orchids have long been an excellent example of how dishonest signal works in plant-animal interaction. Many studies compared the flower structures that resemble female bees, leading toward pseudo-copulation of the male bees on the flower. Using Machine Learning, we tested whether nature is capable of besting artificial intelligence. Read More

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Preliminary Validation of the Triana Test: A New Story Recall Test Based on Emotional Material.

Am J Alzheimers Dis Other Demen 2021 Jan-Dec;36:15333175211025911

Memory Unit, 16885Virgen del Rocio University Hospital, Seville, Spain.

Objective: To first validate the diagnostic accuracy of the "Triana Test," a new story recall test based on emotional material.

Method: A phase I study of validation. We included 55 patients with amnestic Mild Cognitive Impairment and 69 healthy controls, diagnosed according to the "Memory Associative Test of the district of Seine-Saint-Denis" (TMA-93), and matched by age, gender, and educational level. Read More

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Proteomic profiling of low muscle and high fat mass: a machine learning approach in the KORA S4/FF4 study.

J Cachexia Sarcopenia Muscle 2021 Jun 20. Epub 2021 Jun 20.

Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Neuherberg, Germany.

Background: The coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers.

Methods: Data derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13. Read More

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Data-driven point-of-care risk model in patients with acute myocardial infarction and cardiogenic shock.

Eur Heart J Acute Cardiovasc Care 2021 Jun 21. Epub 2021 Jun 21.

Department of Cardiothoracic Anaesthesia, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, staircase 3, 5th floor, 2100 Copenhagen East, Denmark.

Background: Prognosis models based on stepwise regression methods show modest performance in patients with cardiogenic shock (CS). Automated variable selection allows data-driven risk evaluation by recognizing distinct patterns in data. We sought to evaluate an automated variable selection method (least absolute shrinkage and selection operator, LASSO) for predicting 30-day mortality in patients with acute myocardial infarction and CS (AMICS) receiving acute percutaneous coronary intervention (PCI) compared to two established scores. Read More

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Prediction of Impending Septic Shock in Children With Sepsis.

Crit Care Explor 2021 Jun 15;3(6):e0442. Epub 2021 Jun 15.

Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD.

Objectives: Sepsis and septic shock are leading causes of in-hospital mortality. Timely treatment is crucial in improving patient outcome, yet treatment delays remain common. Early prediction of those patients with sepsis who will progress to its most severe form, septic shock, can increase the actionable window for interventions. Read More

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